A Discriminative Model for Polyphonic Piano Transcription

Poliner, Graham E.; Ellis, Daniel P. W.

We present a discriminative model for polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances. The classifier outputs are temporally constrained via hidden Markov models, and the proposed system is used to transcribe both synthesized and real piano recordings. A frame-level transcription accuracy of 68% was achieved on a newly generated test set, and direct comparisons to previous approaches are provided.


Also Published In

EURASIP Journal on Advances in Signal Processing

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Academic Units
Electrical Engineering
Published Here
February 13, 2012